Abstract
Some ecosystems undergo abrupt transitions to a new regime after passing a tipping point in an exogenous stressor, for example lakes shifting from a clear to turbid ‘eutrophic’ state in response to nutrient-enrichment. Metrics-based resilience indicators have been developed as early warning signals of these shifts but have not always proved reliable indicators. Alternative approaches focus on changes in the composition and structure of an ecosystem, which can require long-term food-web observations that are typically beyond the scope of monitoring. Here we prototype a network-based algorithm for estimating ecosystem resilience, which reconstructs past ecological networks solely from palaeoecological abundance data. Resilience is estimated using local stability analysis, and eco-net energy: a neural network-based proxy for ‘ecological memory’. We test the algorithm on modelled (PCLake+) and empirical (lake Erhai) data. The metrics identify increasing diatom community instability during eutrophication in both cases, with eco-net energy revealing complex eco-memory dynamics. The concept of ecological memory opens a new dimension for understanding ecosystem resilience and regime shifts, with eco-memory potentially increasing ecosystem resilience by allowing past memorised eco-network states to be recovered after disruptions.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Revised manuscript to be submitted to PLOS Computational Biology
